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A memory exhaustion issue exists in MOSS-Audio model serving where a processor is cached for every distinct `mm processor kwargs` value within an unbounded dictionary. Unauthenticated attackers can exploit this by sending oversized or distinct `mel config` values to the '/tokenize' endpoint, which bypasses the `--api-key` requirement, leading to host memory exhaustion and a crash of the API process.

When `trust remote code` is enabled, the software forwards the request-supplied `mm processor kwargs.code revision` to the Transformers `AutoProcessor.from pretrained` function. This allows clients to select which revision of a model's dynamic processor code is imported. An attacker who controls a revision in the served model repository can send a multimodal request to the unauthenticated '/tokenize' endpoint to execute arbitrary Python code within the vLLM API process.

An uncontrolled resource consumption issue allows remote clients to manipulate multimodal processor parameters by providing per-request `mm processor kwargs`. Attackers can specify excessive values for image size, patch count, crop count, or frame count within processors such as Isaac, InternVL, and H2OVL to exhaust system memory and cause the shared server to terminate.

The software fails to properly classify DeepStream as a GPU backend, which leads to the omission of pixel-limit enforcement in its decode path. Unauthenticated attackers can activate DeepStream during a request to initialize the process-wide GPU decode pool and submit video content that bypasses resource controls, resulting in a partial denial of service for concurrent requests.